Correlation in R ( NA friendliness, accepting matrix as input, returning p values, visualization, and Pearson vs Spearman)
Many times, in our projects, we may need to compare different measured factors in our samples to one another, and study whether they are linearly dependent. These information can also help us to detect covariates and factors that affect our studies but we would like to adjust for/remove their effects (more on this at sometime later). Here, I mention several functions that can be used to perform correlation tests. All of these functions do support both Pearson and ranked (Spearman) methods. Note that in the end of this post I will focus on these two different methods (i.e. Pearson vs Spearman) and show their differences in application.
2Maintining the data frame fromat when indexing
Occasionally when indexing data frames the format is converted, leading to confusing consequences. As for instance, when indexing to select a single column the result is a 'numeric' or 'integer' vector. The following demonstrates this :
